Abstract
The reasonable allocation and use of human resources is an important content in the process of complex system analysis and design. This paper studies the human resource allocation model of Petri net based on artificial intelligence and neural network. In this paper, combined with the characteristics of human resource scheduling, human resource mobility, concurrency, and obvious classification characteristics, the human resource allocation model based on Petri net is implemented. In this paper, the model is trained with the python version of human resource analysis data set. The training parameters are 100, the error coefficient is 0.001, and the learning speed is 0.01. First, the coding rules of human resource data are established. Then, the parameters are input into the model, and the human resource data are trained in the model. Finally, the results of the model output layer are analyzed. The research study shows that the average prediction accuracy of this model is 78.85%. Model training requires the addition of 25 neurons for every 0.01 increase to improve the accuracy of predicting dynamic data of human resources. If the accuracy rate exceeds 75%, the increase in the number of neurons cannot be compensated for by the increase in the accuracy rate, but it is most efficient when the amount of data for human resource scheduling is 2000 to 4000. Therefore, this system can effectively allocate small- and medium-sized human resources and has a high accuracy.
Highlights
At present, China’s structural reform of human resources is in a critical period, and the role of product innovation in the competitive market and economic reform is gradually highlighted
Human Resource Allocation Based on Petri Net Model
We find that human resource allocation can be influenced by a series of ability, motivation, and opportunity tools, and the internal cognitive mechanism is a direction to be explored
Summary
China’s structural reform of human resources is in a critical period, and the role of product innovation in the competitive market and economic reform is gradually highlighted. It has become an important path choice for enterprises to obtain leapfrog development, sustainable development, high-quality development, and market competitive advantage. Existing studies have confirmed that the effective SHRM system can help enterprises quickly gain market competitive advantage, and the SHRM research can be summarized into two views: configuration view and contingency view. In order to solve the task scheduling problem in the cluster with limited manpower supply, Mom and others proposed a task scheduling method based on task value [1]. Salas vallina first predicted the running time of all tasks to be executed under all possible resource allocation conditions and calculated the value of each task under each resource allocation condition [2]. e algorithm selects the most valuable task and provides the optimal cluster
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